Collaborative Research: Integration of Geographic Complexity and Dynamics into Geographic Information Systems

虽然地理信息科学(GIScience)文献早已认识到地理世界的无限复杂性和动态性，但当前的地理信息系统(GIS)技术尚未包含能够充分处理地理复杂性和动态性的数据模型、查询功能或分析工具。本研究项目旨在将这些功能集成到地理信息系统数据模型、查询和分析中。这种集成将为下一代地理信息系统技术奠定基础，以进一步增强地理信息系统对地理世界的科学理解和发现的支持。为了实现这一整合，调查人员将检查地理复杂性和动态。基本的前提是，地理概念化需要超越地理世界的单独的基于现场和基于对象的视图。研究人员将把重点放在不同空间和时间尺度上嵌入现象和关系中的领域和对象的交织属性所产生的地理复杂性上。他们将考虑反映时空传播和演化的地理动力学，正如拉格朗日动力学(专注于流动的静止作用)或哈密顿动力学(专注于质量粒子的运动)所分析的那样。仅基于现场或对象的概念化不能捕捉到对准确表示地理至关重要的复杂性和动态化。研究人员将加入另外两种地理世界的观点：对象的场(O-场)和场的对象(F-对象)，以结合地理复杂性和动力学。因此，他们希望将双重地理概念化扩展到对象、f对象、o场和场的光谱，其中尺度和分辨率是允许视角沿光谱移动的函数。随着地理概念化的发展，他们将开发一个将地理复杂性和动态与不确定性结合在一起的数据模型，制定查询和分析函数，并建立一个概念验证的原型系统。这个合作项目汇集了来自俄克拉荷马大学、加州大学圣巴巴拉分校和犹他大学的研究人员，以扩展他们在地理空间数据建模方面的工作。在不同的项目中，他们研究了组合字段和对象在地理表示上的使用，并证明了这种组合有效地扩展了地理表示，以纳入更丰富、更复杂的地理语义。该研究项目的核心是基于地理复杂性和动态对GIS数据进行建模的想法，作为传统数据模型的替代方案，传统数据模型建立在如何捕获数据的基础上。该研究项目承诺对领域和对象的综合问题进行更广泛、更全面的审查，并发展地理复杂性和动态表现的整体理论。这种新的GIS数据建模方法将静态表示扩展到复杂而动态的世界视图，从而增强了地理信息系统技术更适合于科学研究。

While the infinite complexity and dynamics of geographic worlds have long been recognized in the geographic information science (GIScience) literature, current geographic information systems (GIS) technology has not yet incorporated data models, query functions, or analytic tools that can adequately handle geographic complexity and dynamics. This research project aims to integrate these features into GIS data models, query, and analysis. Such integration will lay a foundation for the next generations of GIS technology to further empower GIS support for scientific understanding and discovery of geographic worlds. To achieve this integration, the investigators will examine geographic complexity and dynamics. The basic premise is that geographic conceptualizations need to go beyond separate field- and object-based views of geographic worlds. The investigators will focus on geographic complexity that arises from the interwoven properties of fields and objects embedded in phenomena and relationships at different spatial and temporal scales. They will consider geographic dynamics that reflect on propagation and evolution in space and time as analyzed by Lagrangian (focusing on the stationary action of flows) or Hamiltonian (focusing on the motion of a particle of mass) dynamics. Field- or object-based conceptualization alone cannot capture complexity and dynamics critical to an accurate representation of geography. The investigators will incorporate two additional views of geographic worlds: fields of objects (o-fields) and objects of fields (f-objects) to incorporate geographic complexity and dynamics. They therefore expect to extend the dual geographic conceptualization to a spectrum of objects, f-objects, o-fields, and fields, with scale and resolution are as functions that allow a shift in perspective along the spectrum. With the spectrum of geographic conceptualizations, they will develop a data model that incorporates geographic complexity and dynamics with uncertainty, formulates queries and analytical functions, and builds a prototype system for proof of concepts.The collaborative project brings together researchers from the University of Oklahoma, University of California-Santa Barbara, and University of Utah to expand on their work on geospatial data modeling. In separate ventures, they have examined the use of combined fields and objects on geographic representation and demonstrated that such combination effectively extends geographic representation to incorporate much richer, more complex geographic semantics. Central to the research project is the idea of modeling GIS data based on geographic complexity and dynamics, as an alternative to the conventional data models that are built upon how data are captured. The research project promises a broader, more comprehensive inspection of issues related to the integration of fields and objects and the development of a holistic theory of the representation of geographic complexity and dynamics. This new approach to GIS data modeling extends static representation to a complex and dynamic view of the world and thus enhances GIS technology to be better suited for scientific research.